skip to main content
10.1145/1644038.1644049acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Canopy closure estimates with GreenOrbs: sustainable sensing in the forest

Published: 04 November 2009 Publication History

Abstract

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.

References

[1]
FRA 2000 On Definitions of Forest and Forest Change. http://www.fao.org/docrep/006/ad665e/ad665e00.htm.
[2]
GreenOrbs. http://www.greenorbs.org.
[3]
Si photodiode S1087/S1133 Series.
[4]
G. Barrenetxea and G. Schaefer. The hitchhiker's guide to successful wireless sensor network deployments. In Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 43--56. ACM New York, NY, USA, 2008.
[5]
F. Bunnell and D. Vales. Comparison of methods for estimating forest overstory cover: differences among techniques. Canadian Journal of Forest Research, 20(1):101--107, 1990.
[6]
A. Cescatti. Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. II. Model testing and application in a Norway spruce stand. Ecological Modelling, 101(2--3):275--284, 1997.
[7]
P. Dutta, J. Hui, J. Jeong, S. Kim, C. Sharp, J. Taneja, G. Tolle, K. Whitehouse, and D. Culler. Trio: Enabling sustainable and scalable outdoor wireless sensor network deployments. In Proceedings of the 5th international conference on Information processing in sensor networks, pages 407--415. ACM New York, NY, USA, 2006.
[8]
S. Englund, J. O'Brien, and D. Clark. Evaluation of digital and film hemispherical photography and spherical densiometry for measuring forest light environments. Canadian Journal of Forest Research, 30(12):1999--2005, 2000.
[9]
A. Fiala, S. Garman, and A. Gray. Comparison of five canopy cover estimation techniques in the western Oregon Cascades. Forest Ecology and Management, 232(1--3):188--197, 2006.
[10]
R. Grumbine. What is ecosystem management? Conservation Biology, 8(1):27--38, 1994.
[11]
F. James. Monte Carlo theory and practice. Reports on Progress in Physics, 43(9):1145--1189, 1980.
[12]
J. Kang, Y. Zhang, and B. Nath. TARA: Topology-Aware Resource Adaptation to Alleviate Congestion in Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 18(7):919, 2007.
[13]
L. Korhonen, K. Korhonen, M. Rautiainen, and P. Stenberg. Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica, 40(4):577, 2006.
[14]
M. Li and Y. Liu. Underground structure monitoring with wireless sensor networks. In Proceedings of the 6th international conference on Information processing in sensor networks, pages 69--78. ACM New York, NY, USA, 2007.
[15]
A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson. Wireless sensor networks for habitat monitoring.
[16]
D. Moore. The basic practice of statistics. WH Freeman, 2004.
[17]
J. Polastre, R. Szewczyk, and D. Culler. Telos: Enabling ultra-low power wireless research. In Proceedings of the 4th international symposium on Information processing in sensor networks. IEEE Press Piscataway, NJ, USA, 2005.
[18]
V. Ravelomanana. Optimal initialization and gossiping algorithms for random radio networks. IEEE Transactions on Parallel and Distributed Systems, 18(1):17--28, 2007.
[19]
L. Selavo, A. Wood, Q. Cao, T. Sookoor, H. Liu, A. Srinivasan, Y. Wu, W. Kang, J. Stankovic, D. Young, et al. LUSTER: wireless sensor network for environmental research. In Proceedings of the 5th international conference on Embedded networked sensor systems, pages 103--116. ACM New York, NY, USA, 2007.
[20]
G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, et al. A macroscope in the redwoods. In Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 51--63. ACM New York, NY, USA, 2005.
[21]
P. Vicaire, T. He, Q. Cao, T. Yan, G. Zhou, L. Gu, L. Luo, R. Stoleru, J. Stankovic, and T. Abdelzaher. Achieving long-term surveillance in vigilnet. In Proceedings of the 25th IEEE Conference on Computer Communications, 2006.
[22]
G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. Fidelity and yield in a volcano monitoring sensor network. In Proceedings of OSDI, 2006.
[23]
N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. In Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 13--24. ACM New York, NY, USA, 2004.
[24]
L. Zhang, H. Yu, H. Yang, and Z. Zhang. Theoretical Research on a Model for Predicting the Shadow Boundary of an Individual Conical Crown on a Slope. ACTA ECOLOGICA SINICA, 26(010):3317--3323, 2006.

Cited By

View all
  • (2024)Understanding Hidden Knowledge in Generic GraphsIEEE/ACM Transactions on Networking10.1109/TNET.2024.336417732:3(2631-2645)Online publication date: Jun-2024
  • (2024)Orthogonal Rendezvous Multicast for Mobile Sinks in Wireless Sensor Networks2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580317(1104-1109)Online publication date: 8-May-2024
  • (2024)IntroductionLocation, Localization, and Localizability10.1007/978-981-97-3176-3_1(1-8)Online publication date: 12-Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SenSys '09: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
November 2009
438 pages
ISBN:9781605585192
DOI:10.1145/1644038
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2009

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. canopy closure
  2. deployment
  3. design
  4. wireless sensor network

Qualifiers

  • Research-article

Funding Sources

Conference

Acceptance Rates

Overall Acceptance Rate 174 of 867 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)47
  • Downloads (Last 6 weeks)6
Reflects downloads up to 05 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Understanding Hidden Knowledge in Generic GraphsIEEE/ACM Transactions on Networking10.1109/TNET.2024.336417732:3(2631-2645)Online publication date: Jun-2024
  • (2024)Orthogonal Rendezvous Multicast for Mobile Sinks in Wireless Sensor Networks2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580317(1104-1109)Online publication date: 8-May-2024
  • (2024)IntroductionLocation, Localization, and Localizability10.1007/978-981-97-3176-3_1(1-8)Online publication date: 12-Jul-2024
  • (2023)Leggiero: Analog WiFi Backscatter with Payload TransparencyProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596835(436-449)Online publication date: 18-Jun-2023
  • (2023)Enabling Concurrency for Non-orthogonal LoRa ChannelsProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3613268(1-15)Online publication date: 2-Oct-2023
  • (2023)COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle NetworksACM Transactions on Sensor Networks10.1145/357016319:3(1-21)Online publication date: 1-Mar-2023
  • (2023)Understanding Node Localizability in Barycentric Linear LocalizationIEEE/ACM Transactions on Networking10.1109/TNET.2022.321620431:3(1353-1368)Online publication date: Jun-2023
  • (2023)Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)10.1109/ISBP57705.2023.10061307(121-128)Online publication date: 6-Jan-2023
  • (2023)Biopolymer Cryogels for Transient Ecology‐DronesAdvanced Intelligent Systems10.1002/aisy.2023000375:7Online publication date: 27-Apr-2023
  • (2022)On Node Localizability Identification in Barycentric Linear LocalizationACM Transactions on Sensor Networks10.1145/354714319:1(1-26)Online publication date: 8-Dec-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media